Affiliation: Bay Area Environmental Research Institute, NASA Earth eXchange (NEX)

Branch: Biospheric Science Branch (SGE)

Email: akash.awasthi@nasa.gov

Google Scholar: https://scholar.google.com/citations?user=IM6EGDIAAAAJ&hl=en

Dr. Akash Awasthi is a Research Scientist at the NASA Earth eXchange (NEX) at Ames Research Center, where he works within the Wildfire, Ecosystem Resilience, and Risk Assessment (WERK) initiative. His research focuses on developing large multimodal and foundation models for geospatial understanding—integrating vision, language, and physics-based modeling to advance environmental monitoring and Earth system forecasting. At NEX, Dr. Awasthi leads the Built Structure Mapping effort, developing high-resolution AI models to detect and forecast changes in both the built and natural environment. His broader research interests include scientific machine learning, multimodal representation learning, and diffusion-based modeling for scientific applications. Prior to joining NASA, Dr. Awasthi held research positions at Argonne National Laboratory (U.S. Department of Energy) and the University of Houston, where he developed collaborative intelligence systems for radiology and scientific imaging. His research has been featured by the Radiological Society of North America (RSNA) and the University of Houston, and published in leading AI venues such as Radiology: Artificial Intelligence, Knowledge-Based Systems, MICCAI, IEEE ICCV, WACV, and ISBI.

Ph.D., 2025, Electrical and Computer Engineering, University of Houston, TX, USA

B.Tech. (Honors), 2020, Computer Science and Engineering, Kalasalingam University, Tamil Nadu, India*

• (Gold Medalist)

Aug 2025 – Present: Research Scientist, NASA Ames Research Center / BAERI, Moffett Field, CA WERK, NASA Earth eXchange (NEX)

**August 2024 – Dec 2024: Graduate Researcher, **Argonne National Laboratory (U.S. Department of Energy)

May 2024 – Aug 2024: Research Scientist Intern, NASA Ames Research Center / BAERI

Aug 2021 – Nov 2025: Graduate Research Assistant, University of Houston, TX

Jan 2020– May 2020: Student Research Associate, IIT Kanpur, India

Dec 2018 – Jan 2019: Project Trainee, Bhabha Atomic Research Center (BARC), Mumbai, India

Built Structure Mapping for WERK, NASA NEX, 2025–Present Developing large vision models for high-resolution building cover detection across California.

Anomaly Detection in Satellite Videos using Diffusion Models, NASA NEX, 2023

Climate Downscaling with Diffusion Models, Argonne National Laboratory, 2024

Collaborative AI with Large Multimodal Models for Radiology, University of Houston, 2024–2025

Journal Reviewer: Nature Digital Medicine, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Geoscience and Remote Sensing, IEEE Journal of Biomedical and Health Informatics, Nature Scientific Reports, etc

Conference Reviewer/Organizer:

• Reviewer, MICCAI, ISBI, ACCV
• Co-organizer, Emerging LLM/LMM Applications in Medical Imaging (ELAMI) Workshop, MICCAI 2025

Awards and Honors

2025: Featured Work on Collaborative AI, RSNA News (Radiology: AI publication highlight)

2025: Poster Highlight, IEEE/CVF ICCV

2024: Best Poster Finalist, IEEE ISBI 2024

**2020:**Gold Medalist, Kalasalingam University, India

• Awasthi, A., Le, N., Deng, Z., Wu, C.C., & Nguyen, H.V. (2025). Collaborative integration of AI and human expertise to improve detection of chest radiograph abnormalities. Radiology: Artificial Intelligence, e240277. [ Featured in RSNA News]
• Awasthi, A., Chung, B., Vu, A.M., Khan, S., Le, N., Deng, Z., Agrawal, R., Wu, C.C., & Nguyen, H.V. (2025). Structural chain of thoughts for radiology education. Knowledge-Based Systems, 330(Part A), 114433.
• Awasthi, A., Rizvi, S., Peláez, M.J., Wang, Z., Cristini, V., Van Nguyen, H., & Dogra, P. (2024). Deep learning-derived optimal aviation strategies to control pandemics. Scientific Reports, 14(1), 22926.
• Awasthi, A., Le, N., Deng, Z., Agrawal, R., Wu, C.C., & Van Nguyen, H. (2024). Bridging human and machine intelligence: Reverse-engineering radiologist intentions for clinical trust and adoption. Computational and Structural Biotechnology Journal, 24, 711–723.
• Awasthi, A., Vu, A.M., Le, N., Deng, Z., Maulik, S., Agrawal, R., Wu, C.C., & Van Nguyen, H. (2025). Modeling radiologists’ cognitive processes using a digital gaze twin to enhance radiology training. Scientific Reports, 15(1), 13685.
• Awasthi, A., Chung, B.V., Vu, A.M., Le, N., Agrawal, R., Deng, Z., Wu, C., & Nguyen, H.V. (2025). MAARTA: Multi-Agentic Adaptive Radiology Teaching Assistant. In MICCAI 2025, LNCS 15964, Springer, Cham.
• Awasthi, A., Ahmad, S., Le, B., & Nguyen, H. (2024, May). Decoding Radiologists’ Intentions: A Novel System for Accurate Region Identification in Chest X-Ray Image Analysis. IEEE ISBI 2024, 1–5. [Top 1%, Best Poster Award Nomination]
• Pham, T.T., Awasthi, A., Khan, S., Marti, E.D., Nguyen, T.-P., Vo, K., Tran, M., Nguyen, S., Tran, C., Ikebe, Y., Nguyen, A.T., Nguyen, A., Deng, Z., Wu, C.C., Nguyen, H., & Le, N. (2025). Radiology Finding Benchmark for Large Multimodal Models. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV 2025), 21732–21743. [Poster Highlight]
• Pham, T.T., Nguyen, T.P., Ikebe, Y., Awasthi, A., Deng, Z., Wu, C.C., Nguyen, H., & Le, N. (2025). GazeSearch: Radiology findings search benchmark. IEEE/CVF WACV 2025, 96–106. [Oral Presentation]
• Awasthi, A., Ly, S.T., Nizam, J., Mehta, V., Ahmad, S., Nemani, R., Prasad, S., & Van Nguyen, H. (2024, October). Anomaly detection in satellite videos using diffusion models. IEEE MMSP 2024, 1–6. [Oral Presentation]
• Nguyen, H.V., Awasthi, A., Patel, V.M., Le, N., Zhou, Y., Li, S., & Zhou, S.K. (Eds.). (2025). Emerging LLM/LMM Applications in Medical Imaging: ELAMI 2025 Workshop, MICCAI 2025. Springer, Cham.
• Vu, A.N., Tuấn, L., Bùi, N. (Tyler), Le Nguyen, B.N., Awasthi, A., Vo, H., Nguyen, T.-H., Han, Z., Mohan, C., & Nguyen, H.V. (2026). Contrastive Integrated Gradients: A Feature Attribution-Based Method for Explaining Whole Slide Image Classification. IEEE/CVF WACV 2026. [Accepted]
• Awasthi, A., Vamsi, A.M., Duggal, V., Deepalakshmi, P., & Rao, S. (2019, March). 3D Visualization and Localization of Radiation Source in External Radiotherapy Using Inverse Linear Boltzmann Transport Equation. IEEE ICACCS 2019, 123–128. [Best Paper Award]
• Awasthi, A., Kabasares, K.K., Nguyen, H.V., Brosnan, I.G., & Park, T. (2024, December). Refined Urban Mapping: Integrating LiDAR Data and Aerial Imagery for Enhanced Semantic Segmentation of Trees and Buildings. AGU Fall Meeting 2024.